Thermal images-aware guided early fusion network for cross-illumination RGB-T salient object detection. (February 2023) - Wang, Han; Song, Kechen; Huang, Liming; Wen, Hongwei; Yan, Yunhui
Abstract: RGB-T salient object detection (SOD) has been developed rapidly and achieved excellent results in recent years. However, some problems have not yet been solved. The current RGB-T datasets contain only a tiny amount of low-illumination data. The RGB-T SOD method trained based on these RGB-T datasets does not detect the salient objects in extremely low-illumination scenes very well. To improve the detection performance of low-illumination data, we can spend a lot of labor to label low-illumination data, but we tried a new idea to solve the problem by making full use of the properties of Thermal (T) images. Therefore, we propose a T-aware guided early fusion network for cross-illumination salient object detection. Specifically, in the training and testing stage, we use normal illumination data to train our network and then use low and extremely low-illumination data to verify the effectiveness of our method. In the early fusion stage, we propose a T-aware guided module (T-aware) for enhancing salient regions of RGB images at different illumination levels. Secondly, in the decoding stage, we use T images to guide the cross-modal fusion of RGB and T images. In addition, we propose a cross-modal fusion localization-remote correction module (CFL-RCM), which is used to deeply screen and correct redundant information generated by illumination variations. Comparative experiments on the VDT-2048 dataset validate the superior performance of our method on the cross-illumination RGB-T saliency detection. We also obtained favorable results on generalizability experiments with VT5000, VT1000, and VT821 datasets.